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**Unformatted text preview: **Purdue University: ECE438 - Digital Signal Processing with Applications 1 ECE438 - Laboratory 7: Discrete-Time Random Processes (Week 1) October 6, 2010 1 Introduction Many of the phenomena that occur in nature have uncertainty and are best characterized statistically as random processes. For example, the thermal noise in electronic circuits, radar detection, and games of chance are best modeled and analyzed in terms of statistical averages. This lab will cover some basic methods of analyzing random processes. Section 2 reviews some basic definitions and terminology associated with random variables, observations, and estimation. Section 3 investigates a common estimate of the cumulative distribution function. Section 4 discusses the problem of transforming a random variable so that it has a given distribution, and lastly, Section 5 illustrates how the histogram may be used to estimate the probability density function. Note that this lab assumes an introductory background in probability theory. Some review is provided, but it is unfeasible to develop the theory in detail. A secondary reference such as [1] is strongly encouraged. Questions or comments concerning this laboratory should be directed to Prof. Charles A. Bouman, School of Electrical and Computer Engineering, Purdue University, West Lafayette IN 47907; (765) 494- 0340; [email protected] Purdue University: ECE438 - Digital Signal Processing with Applications 2 2 Random Variables The following section contains an abbreviated review of some of the basic definitions associ- ated with random variables. Then we will discuss the concept of an observation of a random event, and introduce the notion of an estimator . 2.1 Basic Definitions A random variable is a function that maps a set of possible outcomes of a random experiment into a set of real numbers. The probability of an event can then be interpreted as the probability that the random variable will take on a value in a corresponding subset of the real line. This allows a fully numerical approach to modeling probabilistic behavior. A very important function used to characterize a random variable is the cumulative distribution function (CDF) , defined as F X ( x ) = P ( X ≤ x ) x ∈ ( −∞ , ∞ ) . (1) Here, X is the random variable, and F X ( x ) is the probability that X will take on a value in the interval ( −∞ , x ]. It is important to realize that x is simply a dummy variable for the function F X ( x ), and is therefore not random at all. The derivative of the cumulative distribution function, if it exists, is known as the prob- ability density function, denoted as f X ( x ). By the fundamental theorem of calculus, the probability density has the following property: integraldisplay t 1 t f X ( x ) dx = F X ( t 1 ) − F X ( t ) (2) = P ( t < X ≤ t 1 ) ....

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